Importação de pacotes¶
In [161]:
import plotly.io as pio
import plotly.graph_objects as go
#pio.renderers.default='notebook'
pio.renderers.default='svg'
layout = {
'width': 1410,
'height': 525
}
from sklearnex import patch_sklearn
patch_sklearn()
import warnings
warnings.filterwarnings('ignore')
import logging
logging.disable(logging.INFO)
Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
In [162]:
from IPython.display import display
import pandas as pd
#import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from sklearn import linear_model, ensemble, set_config
from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
import plotly.express as px
from dataprep.eda import create_report
import xgboost as xgb
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px
from sklearn.tree import plot_tree, DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
import xgboost as xgb
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB, CategoricalNB
import lightgbm as lgb
from catboost import CatBoostClassifier
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.inspection import PartialDependenceDisplay
from sklearn.utils import class_weight
# selecao modelos
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import accuracy_score as acc
from sklearn.metrics import f1_score as f1
from sklearn.metrics import brier_score_loss as brier
from sklearn.metrics import log_loss, precision_score, recall_score
from sklearn import metrics
from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.metrics import RocCurveDisplay, PrecisionRecallDisplay
from category_encoders import CatBoostEncoder, CountEncoder
import lime
set_config(enable_metadata_routing=False)
Importação de dados¶
In [163]:
df = pd.read_csv('iml1_unidade2_dados.csv')
Análise Descritiva¶
In [164]:
display(df.info())
display(df.head(4))
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10127 entries, 0 to 10126 Data columns (total 22 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Unnamed: 0 10127 non-null int64 1 CLIENTNUM 10127 non-null int64 2 Customer_Age 10127 non-null int64 3 Gender 10127 non-null object 4 Dependent_count 10127 non-null int64 5 Education_Level 10127 non-null object 6 Marital_Status 10127 non-null object 7 Income_Category 10127 non-null object 8 Card_Category 10127 non-null object 9 Months_on_book 10127 non-null int64 10 Total_Relationship_Count 10127 non-null int64 11 Months_Inactive_12_mon 10127 non-null int64 12 Contacts_Count_12_mon 10127 non-null int64 13 Credit_Limit 10127 non-null float64 14 Total_Revolving_Bal 10127 non-null int64 15 Avg_Open_To_Buy 10127 non-null float64 16 Total_Amt_Chng_Q4_Q1 10127 non-null float64 17 Total_Trans_Amt 10127 non-null int64 18 Total_Trans_Ct 10127 non-null int64 19 Total_Ct_Chng_Q4_Q1 10127 non-null float64 20 Avg_Utilization_Ratio 10127 non-null float64 21 churn 10127 non-null int64 dtypes: float64(5), int64(12), object(5) memory usage: 1.7+ MB
None
| Unnamed: 0 | CLIENTNUM | Customer_Age | Gender | Dependent_count | Education_Level | Marital_Status | Income_Category | Card_Category | Months_on_book | ... | Contacts_Count_12_mon | Credit_Limit | Total_Revolving_Bal | Avg_Open_To_Buy | Total_Amt_Chng_Q4_Q1 | Total_Trans_Amt | Total_Trans_Ct | Total_Ct_Chng_Q4_Q1 | Avg_Utilization_Ratio | churn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 768805383 | 45 | M | 3 | High School | Married | $60K - $80K | Blue | 39 | ... | 3 | 12691.0 | 777 | 11914.0 | 1.335 | 1144 | 42 | 1.625 | 0.061 | 0 |
| 1 | 1 | 818770008 | 49 | F | 5 | Graduate | Single | Less than $40K | Blue | 44 | ... | 2 | 8256.0 | 864 | 7392.0 | 1.541 | 1291 | 33 | 3.714 | 0.105 | 0 |
| 2 | 2 | 713982108 | 51 | M | 3 | Graduate | Married | $80K - $120K | Blue | 36 | ... | 0 | 3418.0 | 0 | 3418.0 | 2.594 | 1887 | 20 | 2.333 | 0.000 | 0 |
| 3 | 3 | 769911858 | 40 | F | 4 | High School | Unknown | Less than $40K | Blue | 34 | ... | 1 | 3313.0 | 2517 | 796.0 | 1.405 | 1171 | 20 | 2.333 | 0.760 | 0 |
4 rows × 22 columns
Descrição das variáveis:¶
- Customer_Age - idade do cliente em anos
- Gender: gênero (M para Masculino, F para Feminino)
- Dependent_count: Número de dependentes
- Education_Level: qualificação educacional do titular da conta (exemplo: ensino médio, graduação, etc.)
- Marital_Status: estado civil (Casado, Solteiro, Divorciado, Desconhecido)
- Income_Category: categoria de renda anual do titular da conta (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Desconhecido)
- Card_Category: tipo de cartão (Blue, Silver, Gold, Platinum)
- Months_on_book: período de relacionamento com o banco
- Total_Relationship_Count: número total de produtos mantidos pelo cliente
- Months_Inactive_12_mon: número de meses inativo nos últimos 12 meses
- Contacts_Count_12_mon: número de contatos nos últimos 12 meses
- Credit_Limit: limite de crédito no cartão de crédito
- Total_Revolving_Bal: saldo total rotativo no cartão de crédito
- Avg_Open_To_Buy: linha de crédito disponível para compras (Média dos últimos 12 meses)
- Total_Amt_Chng_Q4_Q1: mudança no valor das transações (Q4 sobre Q1)
- Total_Trans_Amt: valor total das transações (Últimos 12 meses)
- Total_Trans_Ct: contagem total de transações (Últimos 12 meses)
- Total_Ct_Chng_Q4_Q1: mudança na contagem de transações (Q4 sobre Q1)
- Avg_Utilization_Ratio: taxa média de utilização do cartão
- Churn: Se o cliente cancelou ou desistiu. (variável de interesse)
Variáveis categóricas:¶
- Gender
- Education_Level
- Marital_Status
- Income_Category
- Card_Category
In [165]:
create_report(df)
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Out[165]: